What Is Grid Computing?

The Grid computing or Grid systems are distributed computing infrastructure, also called large-scale cluster computing, and it is used for processing large amounts of data through the use of a vast amount of resources. In particular, these systems allow the coordinated sharing of resources within a virtual organization.

The GRID

The term “Grid” was coined in the mid-nineties. The real and specific problem underlying the Grid concept is coordinated resource sharing within a dynamic and multi-institutional virtual organization (Virtual Organization, briefly denoted by VO).

The sharing is not limited to the exchange of files, but extends to direct access to computer software, generally all the necessary hardware to solve a scientific problem, engineering or industrial purposes. Individuals and institutions that make available their resources on the grid for the same purpose are part of the same VO.

Common feature of Grid projects is the need for a data-intensive computing environment within which applications have the need to access large amounts of data geographically distributed quickly and reliably and it is precisely the burden of Grid, such applications to operate in the best possible way.

It is easy to see that no computer currently on the market would be able, alone, to produce similar volumes of data in a reasonable time, but the sharing of resources such as CPUs and disks can be properly coordinated to give the impression the user to access a supercomputer virtual, with an incredible computational power and storage capacity capable of handling large workloads.

From idea to bring up the whole architecture of a Grid as a single virtual supercomputer, hiding all the complexity inside the user and show only the benefits, comes the need to design and implement a scheduler resource Resource Broker. It is one of the critical components of the system of resource management, is responsible for allocating resources to the job (Gridlet) so as to meet the needs of applications and system.

The resources it has to include track and manage computing and data storage systems (using the Storage Broker, interconnection network, and through the Network Monitor). The scheduling is a traditional field of information technology. But despite many techniques have been studied for many types of systems (from uniprocessor to multiprocessor distributed systems), the typical characteristics of data grids make many of these approaches inadequate.

Indeed, while in traditional systems of resources and jobs are under the direct control of the scheduler, the grid resources are geographically distributed. These are heterogeneous in nature and belong to different individuals or organizations; each with their own scheduling policies, different cost models of access, workload and availability of resources varies dynamically over time.

The lack of centralized control, along with the presence of users that generate job (Gridlet), very different from each other, make scheduling more complicated than that of traditional computing systems.

The sharing of computational resources enables us to divide the history of computing in 4 eras. The first was characterized by the idea of a single computer for many users. This was the purchase of computing resources has cost so large that the problem is that the contemporary use of these resources by different users. Since the ’80s, hardware costs suffered declines that allowed him to have a computer for each individual user.

Born in this period the first personal computer and the computing infrastructure is evolving toward the SIMD. Since the end of the 80s is beginning to spread the idea of sharing the hardware even said thanks to falling prices, leading to the birth of the first ‘virtual parallel machines’. The 90s are the ones during which applies in full to Moore’s Law and is massive networks of computers and the Internet (basic concepts for the Grid)

Evolution of Grid Computing

The SETI @ home project, launched in 1999 by Dan Werthimer, is a well-known example of a project, albeit simple, Grid computing. SETI @ Home has been followed by many other similar projects in the field of mathematics and microbiology.

Currently, the most important European grid is that of CERN in Geneva is now called EGEE (gLite middleware is the name of produce; LCG earlier and earlier DataGrid), developed – among others – an Italian team-Czech and mainly at the INFN, the National Institute of Nuclear Physics.

In contrast to that used by SETI @ Home, currently a grid is constructed by providing a layer of middleware between the computational resources and memory (CE – Computing Element and SE – storage element) and the users of the grid itself.

The main purpose of middleware is to make the so-called match-making, namely the coupling between the resources required and those available to ensure the distribution of job (the term used to describe a batch system or a part thereof) in always having a better visibility of the status of the entire grid.

Another important phenomenon to emphasize is the birth next to large national and international Grid of multiple deployments on a local or metropolitan distributed system that maintain the characteristics of a GRID. These systems are indicated by the term Local Area Grid (LAG) and Metropolitan Area Grid (MAG) or, more simply, Metropolitan Grid with clear reference to the classification introduced in the network.

The coordination of national Grid provides for the future establishment of a World Wide Grid, the Grid implementations of premises, or subway to the world of the Intranet. In fact, they provide a type of utility projects that can be used simply for the introduction of Internet distributed computing across the enterprise.

The body of reference for the development of uniform standards and protocols used by shouts and GGF (Global Grid Forum) created the standard OGSA (Open Grid Services Architecture). In 2004 it was adopted WSRF (Web Services Resource Framework), which is a set of specifications to help programmers write applications able to access resources on the Grid.

Today the most famous and used software is BOINC, a software for grid computing developed by the University of California (Berkeley). The acronym stands for Berkeley Open BOINC Indeed Infrastructure for Network Computing. This software is open source.

Applications of Grid Computing

An example of the paradigm of Grid computing is neuGRID, a project of the 7th Framework Program, which provides for the development of an infrastructure for the study of neurodegenerative diseases.

GridSim

Thus was developed a graphical interface that allows the user to input the characteristics of the Grid system, which examines the behavior, giving each time the graphic reconstruction. After the first phase, namely the incorporation of features, you start the second phase, relative to the simulation. During the simulation data is processed and the user is presented the report with all the information and responses of the system.

Simulation uses the simulator GridSim while the graphical representation of the system is used in ‘Jung’. JUNG (Java Universal Network / Graph Framework) is a library of open source modeling and visualization of graphs, written in Java.

Study: From Wikipedia, the free encyclopedia. The text is available under the Creative Commons.